Age-associated differences in the cancer molecular landscape
Authors: Kasit Chatsirisupachai, Cyril Lagger, João Pedro de Magalhães
Journal: Trends in cancer (2022)

While the age-related increase in cancer incidence and mortality has been widely recognized, studies of how aging shapes the molecular landscape of tumors have only just begun.Somatic mutations in cancer driver genes are not uniformly distributed across age. Some driver genes are mutated more often in younger or older patients, as revealed by recent pan-cancer studies.Age-associated gene expression and epigenetic landscapes in cancer relate to diverse biological processes such as immune-related processes, extracellular matrix organization, and angiogenesis.Age-related differences in tumor immune landscapes should alter therapeutic responses.Tissue microenvironment changes with age may have a profound impact on cancer. Cancer is an age-related disease, as incidence and mortality for most types of cancer increase with age. However, how molecular alterations in tumors differ among patients of different ages remains poorly understood. Recent studies have shed light on the age-associated molecular landscapes in cancer. Here, we summarize the main findings of these current studies, highlighting major differences in the genomic, transcriptomic, epigenetic, and immunological landscapes between cancer in younger and older patients. Importantly, some cancer driver genes are mutated more frequently in younger or older patients. We discuss the potential roles of aging-related processes in shaping these age-related differences in cancer. We further emphasize the remaining unsolved questions that could provide important insights that will have implications in personalized medicine. Cancer is an age-related disease, as incidence and mortality for most types of cancer increase with age. However, how molecular alterations in tumors differ among patients of different ages remains poorly understood. Recent studies have shed light on the age-associated molecular landscapes in cancer. Here, we summarize the main findings of these current studies, highlighting major differences in the genomic, transcriptomic, epigenetic, and immunological landscapes between cancer in younger and older patients. Importantly, some cancer driver genes are mutated more frequently in younger or older patients. We discuss the potential roles of aging-related processes in shaping these age-related differences in cancer. We further emphasize the remaining unsolved questions that could provide important insights that will have implications in personalized medicine. Aging (see Glossary) involves a progressive deterioration of physiological functions and an increased risk of numerous diseases [1.Franceschi C. et al.The continuum of aging and age-related diseases: common mechanisms but different rates.Front. Med. (Lausanne). 2018; 5: 61Crossref PubMed Scopus (367) Google Scholar]. In particular, an exponential increase in cancer incidence and mortality rate with age has long been recognized [2.Aunan J.R. et al.The biology of aging and cancer: a brief overview of shared and divergent molecular hallmarks.Aging Dis. 2017; 8: 628-642Crossref PubMed Scopus (160) Google Scholar,3.de Magalhaes J.P. How ageing processes influence cancer.Nat. Rev. Cancer. 2013; 13: 357-365Crossref PubMed Scopus (195) Google Scholar]. As the aging population continues to rise, a better understanding of the relationship between aging and cancer is critically needed. In addition, disparities between cancers in young and aged patients have also been observed [4.Van Herck Y. et al.Is cancer biology different in older patients?.Lancet Healthy Longevity. 2021; 2: e663-e677Abstract Full Text Full Text PDF Scopus (16) Google Scholar]. For instance, breast cancer in younger patients tends to be more aggressive and is associated with poorer survival [5.Anders C.K. et al.Young age at diagnosis correlates with worse prognosis and defines a subset of breast cancers with shared patterns of gene expression.J. Clin. Oncol. 2008; 26: 3324-3330Crossref PubMed Scopus (612) Google Scholar], while the prognosis is worse in older ovarian cancer patients [6.Maas H.A. et al.The influence of age and co-morbidity on treatment and prognosis of ovarian cancer: a population-based study.Gynecol. Oncol. 2005; 97: 104-109Abstract Full Text Full Text PDF PubMed Scopus (112) Google Scholar]. Several studies have revealed distinct molecular characteristics of tumors in relation to age in various cancer types, such as breast [7.Kan Z. et al.Multi-omics profiling of younger Asian breast cancers reveals distinctive molecular signatures.Nat. Commun. 2018; 9: 1725Crossref PubMed Scopus (82) Google Scholar,8.Liao S. et al.The molecular landscape of premenopausal breast cancer.Breast Cancer Res. 2015; 17: 104Crossref PubMed Scopus (43) Google Scholar], prostate [9.Gerhauser C. et al.Molecular evolution of early-onset prostate cancer identifies molecular risk markers and clinical trajectories.Cancer Cell. 2018; 34: 996-1011 e8Abstract Full Text Full Text PDF PubMed Scopus (110) Google Scholar], and colorectal [10.Lieu C.H. et al.Comprehensive genomic landscapes in early and later onset colorectal cancer.Clin. Cancer Res. 2019; 25: 5852-5858Crossref PubMed Scopus (66) Google Scholar] cancers. These analyses, however, focused on one cancer type and only a few molecular data types at a time. Recently, four independent studies performed pan-cancer analyses to shed light on the age-associated genomic, transcriptomic, and epigenomic patterns [11.Chatsirisupachai K. et al.An integrative analysis of the age-associated multi-omic landscape across cancers.Nat. Commun. 2021; 12: 2345Crossref PubMed Scopus (19) Google Scholar, 12.Li C.H. et al.Age influences on the molecular presentation of tumours.Nat. Commun. 2022; 13: 208Crossref PubMed Scopus (4) Google Scholar, 13.Shah Y. et al.Pan-cancer analysis reveals molecular patterns associated with age.Cell Rep. 2021; 37110100Abstract Full Text Full Text PDF PubMed Scopus (6) Google Scholar, 14.Lee W. et al.Genomic and molecular features distinguish young adult cancer from later-onset cancer.Cell Rep. 2021; 37110005Abstract Full Text Full Text PDF Scopus (10) Google Scholar]. The age-related patterns of molecular alterations might suggest differences in the oncogenic mechanisms concerning the patient's age. Another pan-cancer study focused on age-related markers of immune checkpoint blockade (ICB) and a shift in immune-cell-type abundance with age, which will be crucial for designing immunotherapy strategies [15.Erbe R. et al.Evaluating the impact of age on immune checkpoint therapy biomarkers.Cell Rep. 2021; 36109599Abstract Full Text Full Text PDF PubMed Scopus (6) Google Scholar]. Here, we summarize the major findings from these pan-cancer and cancer-specific studies (Table S1 in the supplemental information online) and discuss potential aging processes that might contribute to these differences in cancer molecular landscape. Increased age is associated with higher somatic mutations (single-nucleotide variants and small insertions/deletions) in most cancer types [11.Chatsirisupachai K. et al.An integrative analysis of the age-associated multi-omic landscape across cancers.Nat. 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Genet. 2015; 47: 1402-1407Crossref PubMed Scopus (533) Google Scholar], with an estimated increase of 0.077 mutations per megabase per year [12.Li C.H. et al.Age influences on the molecular presentation of tumours.Nat. Commun. 2022; 13: 208Crossref PubMed Scopus (4) Google Scholar]. The spontaneous deamination of 5-methylcytosine to thymine (C>T) transitions, often referred to as the 'clock-like' mutational signature, dominates this age-related increase in mutation load. Furthermore, DNA damage repair signatures are more likely to be found in older individuals [12.Li C.H. et al.Age influences on the molecular presentation of tumours.Nat. Commun. 2022; 13: 208Crossref PubMed Scopus (4) Google Scholar]. The mutational signature related to APOBEC (apolipoprotein B mRNA editing enzyme, catalytic polypeptide-like family) cytidine deaminase activity increases with age in melanoma [12.Li C.H. et al.Age influences on the molecular presentation of tumours.Nat. Commun. 2022; 13: 208Crossref PubMed Scopus (4) Google Scholar] and prostate cancer [9.Gerhauser C. et al.Molecular evolution of early-onset prostate cancer identifies molecular risk markers and clinical trajectories.Cancer Cell. 2018; 34: 996-1011 e8Abstract Full Text Full Text PDF PubMed Scopus (110) Google Scholar]. In addition to somatic mutations, somatic copy-number alterations (SCNAs) also increase as a function of age in the pan-cancer analysis (Figure 1) [11.Chatsirisupachai K. et al.An integrative analysis of the age-associated multi-omic landscape across cancers.Nat. Commun. 2021; 12: 2345Crossref PubMed Scopus (19) Google Scholar,12.Li C.H. et al.Age influences on the molecular presentation of tumours.Nat. Commun. 2022; 13: 208Crossref PubMed Scopus (4) Google Scholar]. Cancer-type-specific analyses revealed a significant positive association between SCNA level and age in a few cancer types, including low-grade glioma, endometrial, ovarian, and prostate cancers [11.Chatsirisupachai K. et al.An integrative analysis of the age-associated multi-omic landscape across cancers.Nat. Commun. 2021; 12: 2345Crossref PubMed Scopus (19) Google Scholar,12.Li C.H. et al.Age influences on the molecular presentation of tumours.Nat. Commun. 2022; 13: 208Crossref PubMed Scopus (4) Google Scholar]. However, another study found a positive association between SCNAs and age only in sarcoma [14.Lee W. et al.Genomic and molecular features distinguish young adult cancer from later-onset cancer.Cell Rep. 2021; 37110005Abstract Full Text Full Text PDF Scopus (10) Google Scholar]. This discrepancy might be due to the use of a 50-year-old cutoff to separate young and old patients [14.Lee W. et al.Genomic and molecular features distinguish young adult cancer from later-onset cancer.Cell Rep. 2021; 37110005Abstract Full Text Full Text PDF Scopus (10) Google Scholar], in contrast to other studies that used age as a continuous variable in their statistical model [11.Chatsirisupachai K. et al.An integrative analysis of the age-associated multi-omic landscape across cancers.Nat. Commun. 2021; 12: 2345Crossref PubMed Scopus (19) Google Scholar,12.Li C.H. et al.Age influences on the molecular presentation of tumours.Nat. Commun. 2022; 13: 208Crossref PubMed Scopus (4) Google Scholar]. Tumors from older individuals tend to accumulate more clonal mutations (i.e., mutations that arise earlier in tumor evolution) [12.Li C.H. et al.Age influences on the molecular presentation of tumours.Nat. Commun. 2022; 13: 208Crossref PubMed Scopus (4) Google Scholar]. This observation could correspond to an accumulation of somatic mutations with age occurring before carcinogenesis, as recently reported in most noncancerous human tissues [19.Kakiuchi N. Ogawa S. Clonal expansion in non-cancer tissues.Nat. Rev. Cancer. 2021; 21: 239-256Crossref PubMed Scopus (48) Google Scholar, 20.Moore L. et al.The mutational landscape of normal human endometrial epithelium.Nature. 2020; 580: 640-646Crossref PubMed Scopus (174) Google Scholar, 21.Martincorena I. et al.Somatic mutant clones colonize the human esophagus with age.Science. 2018; 362: 911-917Crossref PubMed Scopus (494) Google Scholar]. Further studies are needed to elucidate how age affects clonal and subclonal mutations in cancer to better understand the impact of age on cancer evolution. While mutation load increases with age in most cancer types, lung adenocarcinoma and endometrial cancer show an opposite trend. SCNAs also decrease with age in lung adenocarcinoma. The fact that smokers were diagnosed with lung cancer at younger ages in The Cancer Genome Atlas (TCGA) cohort potentially explains the negative association between age and somatic mutation and SCNA in patients with lung cancer [11.Chatsirisupachai K. et al.An integrative analysis of the age-associated multi-omic landscape across cancers.Nat. Commun. 2021; 12: 2345Crossref PubMed Scopus (19) Google Scholar,12.Li C.H. et al.Age influences on the molecular presentation of tumours.Nat. Commun. 2022; 13: 208Crossref PubMed Scopus (4) Google Scholar], although other unexplored causes are likely to contribute as well. For endometrial cancer, tumors from younger patients showed a higher proportion of the high microsatellite instability (MSI-H) subtype [22.Campbell B.B. et al.Comprehensive analysis of hypermutation in human cancer.Cell. 2017; 171: 1042-1056 e10Abstract Full Text Full Text PDF PubMed Scopus (446) Google Scholar]. Furthermore, mutations in DNA polymerase ε (POLE) and polymerase δ (POLD1) are found more often in younger endometrial cancer patients [11.Chatsirisupachai K. et al.An integrative analysis of the age-associated multi-omic landscape across cancers.Nat. Commun. 2021; 12: 2345Crossref PubMed Scopus (19) Google Scholar,14.Lee W. et al.Genomic and molecular features distinguish young adult cancer from later-onset cancer.Cell Rep. 2021; 37110005Abstract Full Text Full Text PDF Scopus (10) Google Scholar,23.Ashley C.W. et al.Analysis of mutational signatures in primary and metastatic endometrial cancer reveals distinct patterns of DNA repair defects and shifts during tumor progression.Gynecol. Oncol. 2019; 152: 11-19Abstract Full Text Full Text PDF PubMed Scopus (51) Google Scholar]. Why the MSI-H and POLE/POLD1 mutation subtypes occur more frequently in younger patients is, however, still unclear. Several cancer types display an age-associated mutational landscape in known cancer driver genes. In other words, some driver genes are mutated more often in younger or older individuals (Figure 1). A prominent example of this is a much higher frequency of mutations in isocitrate dehydrogenase 1 (IDH1), alpha thalassemia/mental retardation syndrome X-linked gene (ATRX), and tumor protein p53 (TP53) in younger glioma patients. These mutations are associated with the IDH-mutant subtype [11.Chatsirisupachai K. et al.An integrative analysis of the age-associated multi-omic landscape across cancers.Nat. Commun. 2021; 12: 2345Crossref PubMed Scopus (19) Google Scholar,12.Li C.H. et al.Age influences on the molecular presentation of tumours.Nat. Commun. 2022; 13: 208Crossref PubMed Scopus (4) Google Scholar,14.Lee W. et al.Genomic and molecular features distinguish young adult cancer from later-onset cancer.Cell Rep. 2021; 37110005Abstract Full Text Full Text PDF Scopus (10) Google Scholar,24.Ceccarelli M. et al.Molecular profiling reveals biologically discrete subsets and pathways of progression in diffuse glioma.Cell. 2016; 164: 550-563Abstract Full Text Full Text PDF PubMed Scopus (1195) Google Scholar]. One study found that mutations in ATRX are an age-dependent prognostic biomarker for low-grade glioma; such mutations are associated with a poor outcome in younger patients but with better survival in older patients [12.Li C.H. et al.Age influences on the molecular presentation of tumours.Nat. Commun. 2022; 13: 208Crossref PubMed Scopus (4) Google Scholar]. Conversely, the IDH-wild-type subtype associated with copy-number losses of chromosome 10 (PTEN) and gains of chromosome 7 (EGFR) is higher in older glioma patients [11.Chatsirisupachai K. et al.An integrative analysis of the age-associated multi-omic landscape across cancers.Nat. Commun. 2021; 12: 2345Crossref PubMed Scopus (19) Google Scholar,14.Lee W. et al.Genomic and molecular features distinguish young adult cancer from later-onset cancer.Cell Rep. 2021; 37110005Abstract Full Text Full Text PDF Scopus (10) Google Scholar,25.Korber V. et al.Evolutionary trajectories of IDH(WT) glioblastomas reveal a common path of early tumorigenesis instigated years ahead of diagnosis.Cancer Cell. 2019; 35: 692-704 e12Abstract Full Text Full Text PDF PubMed Scopus (100) Google Scholar]. As another example already mentioned above, younger endometrial cancers are associated with MSI-H and POLE/POLD1 mutation subtypes with a high mutation load. Thus, younger patients contain a higher percentage of somatic mutations in cancer driver genes, including DNA-repair genes, PI3KCA (phosphatidylinositol-4,5-bisphosphate 3-kinase catalytic subunit α), and growth factor signaling pathways [13.Shah Y. et al.Pan-cancer analysis reveals molecular patterns associated with age.Cell Rep. 2021; 37110100Abstract Full Text Full Text PDF PubMed Scopus (6) Google Scholar]. However, older endometrial tumors present a very high SCNA burden in several regions, including regions harboring cancer driver genes [11.Chatsirisupachai K. et al.An integrative analysis of the age-associated multi-omic landscape across cancers.Nat. Commun. 2021; 12: 2345Crossref PubMed Scopus (19) Google Scholar], and are associated with poorer survival than in younger patients [26.Levine, D.A. and Cancer Genome Atlas Research Network Integrated genomic characterization of endometrial carcinoma.Nature. 2013; 497: 67-73Crossref PubMed Scopus (2929) Google Scholar]. Breast cancer is potentially the most well-characterized cancer in terms of age-associated subtyping. A favorable prognostic estrogen receptor-positive (ER+) subtype is diagnosed more often in older individuals, while an aggressive human epidermal growth factor receptor 2-positive (HER2+) subtype is more common in younger patients. In the PAM50 subtyping system, younger women are diagnosed with more biologically aggressive HER2-enriched and basal-like subtypes [27.Sweeney C. et al.Intrinsic subtypes from PAM50 gene expression assay in a population-based breast cancer cohort: differences by age, race, and tumor characteristics.Cancer Epidemiol. Biomark. Prev. 2014; 23: 714-724Crossref PubMed Scopus (78) Google Scholar]. Luminal A tumors that have a better prognosis are more common in older women [4.Van Herck Y. et al.Is cancer biology different in older patients?.Lancet Healthy Longevity. 2021; 2: e663-e677Abstract Full Text Full Text PDF Scopus (16) Google Scholar]. Regarding somatic mutations, higher cadherin 1 (CDH1) mutations in older patients are observed [7.Kan Z. et al.Multi-omics profiling of younger Asian breast cancers reveals distinctive molecular signatures.Nat. Commun. 2018; 9: 1725Crossref PubMed Scopus (82) Google Scholar,8.Liao S. et al.The molecular landscape of premenopausal breast cancer.Breast Cancer Res. 2015; 17: 104Crossref PubMed Scopus (43) Google Scholar,11.Chatsirisupachai K. et al.An integrative analysis of the age-associated multi-omic landscape across cancers.Nat. Commun. 2021; 12: 2345Crossref PubMed Scopus (19) Google Scholar,13.Shah Y. et al.Pan-cancer analysis reveals molecular patterns associated with age.Cell Rep. 2021; 37110100Abstract Full Text Full Text PDF PubMed Scopus (6) Google Scholar]. The CDH1 mutation is highly enriched in the invasive lobular carcinoma subtype, which is more common in older patients [28.Berger A.C. et al.A comprehensive pan-cancer molecular study of gynecologic and breast cancers.Cancer Cell. 2018; 33: 690-705 e9Abstract Full Text Full Text PDF PubMed Scopus (291) Google Scholar]. Mutations in PIK3CA also appear to increase in frequency with age [29.Mealey N.E. et al.Mutational landscape differences between young-onset and older-onset breast cancer patients.BMC Cancer. 2020; 20: 212Crossref PubMed Scopus (9) Google Scholar,30.Waks A.G. et al.Somatic and germline genomic alterations in very young women with breast cancer.Clin. Cancer Res. 2022; 28: 2339-2348Crossref PubMed Scopus (4) Google Scholar]. Breast cancer in younger patients is associated with higher TP53, GATA binding protein 3 (GATA3), and AT-rich interaction domain 1A (ARID1A) mutations [7.Kan Z. et al.Multi-omics profiling of younger Asian breast cancers reveals distinctive molecular signatures.Nat. Commun. 2018; 9: 1725Crossref PubMed Scopus (82) Google Scholar,13.Shah Y. et al.Pan-cancer analysis reveals molecular patterns associated with age.Cell Rep. 2021; 37110100Abstract Full Text Full Text PDF PubMed Scopus (6) Google Scholar,30.Waks A.G. et al.Somatic and germline genomic alterations in very young women with breast cancer.Clin. Cancer Res. 2022; 28: 2339-2348Crossref PubMed Scopus (4) Google Scholar,31.Azim Jr., H.A. et al.Genomic aberrations in young and elderly breast cancer patients.BMC Med. 2015; 13: 266Crossref PubMed Scopus (70) Google Scholar]. Interestingly, a recent report suggests that the age-associated differences in GATA3, ARID1A, and PIK3CA mutations were only found in luminal A but not in other PAM50 subtypes [30.Waks A.G. et al.Somatic and germline genomic alterations in very young women with breast cancer.Clin. Cancer Res. 2022; 28: 2339-2348Crossref PubMed Scopus (4) Google Scholar]. The identification of age-related driver genes may be clinically relevant. For instance, PIK3CA mutations, which are more common in older patients, correlate with a better treatment outcome in early-stage breast cancer [32.Zardavas D. et al.Tumor PIK3CA genotype and prognosis in early-stage breast cancer: a pooled analysis of individual patient data.J. Clin. Oncol. 2018; 36: 981-990Crossref PubMed Scopus (64) Google Scholar]. Next, mutations in GATA3, a gene encoding transcription factor that acts cooperatively with ER and is mutated more frequently in younger patients, could promote tumor cell growth and associate with endocrine resistance [33.Adomas A.B. et al.Breast tumor specific mutation in GATA3 affects physiological mechanisms regulating transcription factor turnover.BMC Cancer. 2014; 14: 278Crossref PubMed Scopus (42) Google Scholar]. Furthermore, lower GATA3 expression is associated with poor prognosis [34.Mehra R. et al.Identification of GATA3 as a breast cancer prognostic marker by global gene expression meta-analysis.Cancer Res. 2005; 65: 11259-11264Crossref PubMed Scopus (235) Google Scholar]. However, mutations in GATA3 can be both gain-of-function and loss-of-function [35.Takaku M. et al.GATA3 zinc finger 2 mutations reprogram the breast cancer transcriptional network.Nat. Commun. 2018; 9: 1059Crossref PubMed Scopus (53) Google Scholar], and it remains unclear whether GATA3 acts as a tumor suppressor or as an oncogene [36.Takaku M. et al.GATA3 in breast cancer: tumor suppressor or oncogene?.Gene Expr. 2015; 16: 163-168Crossref PubMed Scopus (63) Google Scholar]. Therefore, further studies are required to better clarify biological and clinical implications of GATA3 mutations in younger and older breast cancer patients. For other cancer types, it has been reported that TP53 and CTNNB1 mutations are more common in younger colorectal cancer patients, while adenomatous polyposis coli (APC), Kirsten rat sarcoma viral oncogene homolog (KRAS), and v-raf murine sarcoma viral oncogene homolog B1 (BRAF) V600 mutations are higher in older patients [10.Lieu C.H. et al.Comprehensive genomic landscapes in early and later onset colorectal cancer.Clin. Cancer Res. 2019; 25: 5852-5858Crossref PubMed Scopus (66) Google Scholar,37.Willauer A.N. et al.Clinical and molecular characterization of early-onset colorectal cancer.Cancer. 2019; 125: 2002-2010Crossref PubMed Scopus (118) Google Scholar,38.Berg M. et al.DNA sequence profiles of the colorectal cancer critical gene set KRAS-BRAF-PIK3CA-PTEN-TP53 related to age at disease onset.PLoS One. 2010; 5e13978Crossref Google Scholar]. By contrast, BRAF mutations, especially BRAF V600, decrease with age in melanoma [39.Bauer J. et al.BRAF mutations in cutaneous melanoma are independently associated with age, anatomic site of the primary tumor, and the degree of solar elastosis at the primary tumor site.Pigment Cell Melanoma Res. 2011; 24: 345-351Crossref PubMed Scopus (175) Google Scholar]. CDH1 mutations, which are higher in older breast cancer patients, decrease with age in stomach cancer [11.Chatsirisupachai K. et al.An integrative analysis of the age-associated multi-omic landscape across cancers.Nat. Commun. 2021; 12: 2345Crossref PubMed Scopus (19) Google Scholar]. In prostate cancer, structural variation breakpoints were highly enriched near gene regulatory regions such as active enhancers in the early-onset but not late-onset prostate cancer [9.Gerhauser C. et al.Molecular evolution of early-onset prostate cancer identifies molecular risk markers and clinical trajectories.Cancer Cell. 2018; 34: 996-1011 e8Abstract Full Text Full Text PDF PubMed Scopus (110) Google Scholar]. Overall, somatic mutations in cancer driver genes do not uniformly distribute across age. These age-related somatic mutations appear to be cancer-type specific. In addition, some mutations display opposite trends in different cancers. Typically, mutations in BRAF V600 decline with age in melanoma and increase in colorectal cancer. Mutations in BRAF V600 are an example of age-related mutations that are clinically actionable targets with multiple approved drugs [14.Lee W. et al.Genomic and molecular features distinguish young adult cancer from later-onset cancer.Cell Rep. 2021; 37110005Abstract Full Text Full Text PDF Scopus (10) Google Scholar]. Additional investigation of age-related driver genes could shed light on the underlying biological differences between tumors from younger and older groups and help improve treatment strategy. In addition, further studies are needed to determine if the effectiveness of targeted drugs differs according to patient's age [4.Van Herck Y. et al.Is cancer biology different in older patients?.Lancet Healthy Longevity. 2021; 2: e663-e677Abstract Full Text Full Text PDF Scopus (16) Google Scholar]. Besides, older patients often present with comorbidities and may have specific therapeutic challenges [40.Balachandran K. Kalsi T. The impact of age on assessment and treatment of breast cancer in older people: you are only as old as your oncologist thinks you are.Clin. Oncol. 2022; 34: 363-367Abstract Full Text Full Text PDF Scopus (1) Google Scholar]. In summary, the identification of age-related somatic driver mutations and further investigation of these drivers could have a profound impact in the clinical setting to improve treatment options for patients of different age groups. Several studies attempted to investigate age-related gene expression in cancer. In breast cancer, an early study reported a higher expression of cell cycle-related genes in tumors from younger than those from older ER+ patients [41.Yau C. et al.Aging impacts transcriptomes but not genomes of hormone-dependent breast cancers.Breast Cancer Res. 2007; 9: R59Crossref PubMed Scopus (52) Google Scholar]. Recently, another work suggested that age-related differentially expressed genes in breast cancer could partly be controlled by age-related changes in estrogen signaling [42.Osako T. et al.Age-correlated protein and transcript expression in breast cancer and normal breast tissues is dominated by host endocrine effects.Nat. Cancer. 2020; 1: 518-532Crossref PubMed Scopus (8) Google Scholar]. Pan-cancer studies reported that the amount of age-associated differentially expressed genes in cancer varies across tumor types. These genes are partly regulated by age-associated DNA methylation changes [11.Chatsirisupachai K. et al.An integrative analysis of the age-associated multi-omic landscape across cancers.Nat. Commun. 2021; 12: 2345Crossref PubMed Scopus (19) Google Scholar,13.Shah Y. et al.Pan-cancer analysis reveals molecular patterns associated with age.Cell Rep. 2021; 37110100Abstract Full Text Full Text PDF PubMed Scopus (6) Google Scholar,43.Wu Y. et al.Comprehensive transcriptome profiling in elderly cancer patients reveals aging-altered immune cells and immune checkpoints.Int. J. Cancer. 2019; 144: 1657-1663Crossref PubMed Scopus (13) Google Scholar]. One study used survival data and the number of differentially expressed genes to classify cancers as age-associated and non-age-associated [13.Shah Y. et al.Pan-cancer analysis reveals molecular patterns associated with age.Cell Rep. 2021; 37110100Abstract Full Text Full Text PDF PubMed Scopus (6) Google Scholar]. Age-associated cancer types include low-grade glioma, lung squamous cell carcinoma, thyroid adenocarcinoma, and cancers of female reproductive organs (breast, ovarian, and endometrial). Notably, tumors from younger patients of age-associated cancers are associated with increased age acceleration as measured by the epigenetic clock. This was not observed in non-age-associated cancers [13.Shah Y. et al.Pan-cancer analysis reveals molecular patterns associated with age.Cell Rep. 2021; 37110100Abstract Full Text Full Text PDF PubMed Scopus (6) Google Scholar]. Nevertheless, the molecular mechanisms behind this observation, and why such a feature is limited to only age-associated cancers, are unclear. The observed age-related gene expression changes in tumors are associated with numerous biological processes, such as extracellular matrix (ECM) organization, metabolism, development, signaling pathways, and immune-related processes across various cancer types [7.Kan Z. et al.Multi-omics profiling of younger Asian breast cancers reveals distinctive molecular signatures.Nat. Commun. 2018; 9: 1725Crossref PubMed Scopus (82) Google Scholar,8.Liao S. et al.The molecular landscap